What is the clinical evidence from this study?

Study design: Cohort. Population: Cancer (breast, prostate, colorectal, and lung) (n=1064948). Intervention: Polygenic risk scores (PRS-CSx, Lassosum, mJAM, standard GWAS weights) vs. Comparison between PRS weighting strategies. Primary outcome: PRS performance for disease risk prediction.

What question did this study set out to answer?

The study aims to assess how different PRS construction methods affect cancer risk prediction across diverse cohorts.

April 5, 2026

Abstract 3584: Characterizing polygenic risk scores for cancer in prospective cohorts: Considering PRS method construction, age of diagnosis, genetic ancestry, and sample overlap.

Key Result

The choice of polygenic risk score construction method and target cohort characteristics substantially influenced disease risk characterization for four major cancers across over a million subjects.

Key Points

The study aims to assess how different PRS construction methods affect cancer risk prediction across diverse cohorts.
Compared four PRS weighting strategies across eight large prospective cohorts.
Evaluated PRS performance for breast, prostate, colorectal, and lung cancers.
Included various demographic factors like genetic ancestry and age of diagnosis.
Analyzed over a million subjects with diverse racial and ethnic backgrounds.
Demonstrated significant variation in PRS effectiveness based on the chosen weighting method.
Found that cohort characteristics substantially influenced disease risk assessment.
Integrated multiple PRS approaches to enhance understanding of genetic and non-genetic factors in cancer risk.

Study Design

Type

Cohort (n=1,064,948)

Multicenter

Yes

Structured PICO

Does the choice of polygenic risk score construction method affect risk prediction performance for major cancers across diverse prospective cohorts?

Population

Over a million subjects from eight large, prospective cohorts (MEC n=73,139, GERA n=103,358, WHI n=46,794, NHS n=20,195, NHS2 n=16,082, HPFS n=12,649, UKB n=474,775, AoU n=317,956) spanning multiple racial and ethnic groups and broad age distributions.

Intervention

Four polygenic risk score (PRS) weighting strategies: PRS-CSx, Lassosum, multi-ethnic joint analysis of marginal summary statistics (mJAM) forward selection procedure, and standard GWAS derived weights.

Comparator

Comparison among the four PRS weighting strategies.

Outcome

PRS performance for four major cancers (breast, prostate, colorectal, and lung).

The choice of polygenic risk score construction method and target cohort characteristics significantly impact cancer risk prediction performance.

Abstract

Abstract Well-established prospective cancer cohorts provide a unique opportunity to investigate the combined use of polygenic risk scores (PRSs) and non-genetic factors for personalized disease risk prediction. However, their application can vary depending on cohort-specific characteristics. We compared four PRS weighting strategies: PRS-CSx, Lassosum, a multi-ethnic joint analysis of marginal summary statistics (mJAM) forward selection procedure, and standard genome-wide association study (GWAS) derived weights, across eight large, prospective cohorts, totaling over a million subjects from the Multiethnic Cohort (MEC, n=73,139), the Genetic Epidemiology Research on Aging (GERA, n=103,358), the Women’s Health Initiative (WHI, n=46,794), the Nurses’ Health Studies I (NHS, n=20,195) and II (NHS2, n=16,082), the Health Professionals Follow-up Study (HPFS, n=12,649), the UK Biobank (UKB, n=474,775) and All of Us (AoU, n=317,956). Together, these cohorts represent a uniquely diverse study population spanning multiple racial and ethnic groups, broad age distributions, and both overlapping and non-overlapping samples with discovery GWAS populations. We evaluated PRS performance for four major cancers (breast, prostate, colorectal, and lung) across all weighting methods and contexts. Our findings show that both the choice of PRS construction method and the characteristics of the target cohort substantially influence the characterization of disease risk. By integrating four distinct PRS approaches across eight large and diverse prospective cohorts, this study provides one of the most comprehensive evaluations to date of context-dependent variation and provides guidance for ongoing development of risk prediction models that combine genetic and non-genetic factors. Citation Format: Julie-Alexia Dias, Gillian King, David Bogumil, Brian Huang, Fei Chen, David V. Conti. Characterizing polygenic risk scores for cancer in prospective cohorts: Considering PRS method construction, age of diagnosis, genetic ancestry, and sample overlap abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 3584.

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